4 Authors: Rebecca Landy 1, David Walsh 2, Julie Ramsay 3 1 Economic and Social Research Council intern, based with Scottish Government 2 Glasgow Centre for Population Health 3 Scottish Government Crown copyright 2010 ISBN: ISSN: The Scottish Government St Andrew s House Edinburgh EH1 3DG Produced for the Scottish Government by APS Group Scotland DPPAS10908 Published by the Scottish Government, November 2010

8 Authors Acknowledgements Our first thank you is to the 14,000 men and women in Scotland who gave up their time voluntarily to take part in the surveys analysed here. We are also grateful to the interviewers who conducted the surveys for the dedication and professionalism they applied to their work. We would also like to thank Joan Corbett at the Scottish Centre for Social Research who patiently answered many questions on the data, and John D Souza at the National Centre for Social Research who answered technical questions regarding weights. Responsibility for all analyses and conclusions lies with the authors. Rebecca Landy, David Walsh and Julie Ramsay 5

9 Summary The link between socio-economic circumstances and health is well known, and there is an increasing evidence base supporting the hypothesis of a Scottish Effect, and more specifically a Glasgow Effect, the terminology used to identify higher levels of mortality and poor health found in Scotland and Glasgow beyond that explained by socio-economic circumstances. The last study which investigated the existence of a Glasgow Effect in a wide range of health behaviours and outcomes used data from the 1995, 1998 and 2003 Scottish Health Surveys, using the Carstairs measure of area-based deprivation, which is less spatially sensitive than the Scottish Index of Multiple Deprivation (SIMD) now available. Additionally, that study only investigated whether socio-economic factors explained any differences between Glasgow and the rest of Scotland, and did not investigate other potential explanations. This report therefore both updates and extends that work by using the 2008 and 2009 Scottish Health Survey data. The overall aim of this work was to investigate whether residence in Glasgow was independently associated with poorer health outcomes and worse health behaviours compared to the rest of Scotland, after controlling for socio-economic, behavioural, biological and other health-related risk factors. To accomplish this aim a series of logistic regression models were carried out for a variety of adverse health behaviours and mental and physical health outcomes, and the extent to which any observed differences between Greater Glasgow and Clyde and the rest of Scotland were explained was examined by first adjusting for age and sex, then additionally adjusting for area level deprivation using the Scottish Index of Multiple Deprivation (SIMD), individual level socio-economic factors, behavioural, biological, relationship and social mobility variables. This study showed that combined area and individual level socio-economic circumstances explained the differences found between Greater Glasgow and Clyde and the rest of Scotland for the majority of outcomes investigated. However four outcomes remained where differences were not explained by socio-economic factors: anxiety, doctor diagnosed heart attack, high GHQ scores, and being overweight. Of these, the latter two were explained by differences in biological factors. However there remained an unexplained Glasgow Effect in relation to prevalence of anxiety and doctor diagnosed heart attack, with higher levels found in Greater Glasgow and Clyde. Therefore further research is needed into the reasons behind the increased levels of anxiety and heart attacks found in Greater Glasgow and Clyde. 6

10 1. INTRODUCTION AND METHODOLOGY 1.1 Introduction The link between socio-economic circumstances and health is well known, and has been widely investigated, with deprivation found to be a key factor for a variety of health outcomes. One such health outcome is mortality. Scotland has the highest mortality rate in western Europe among the working age population, and has done since the late 1970s 1. Carstairs and Morris 2 analysed data from investigating whether social class and deprivation could explain the excess mortality experienced by Scotland compared to England and Wales. They found that standardising for social class had little effect, whereas standardising for relative affluence and deprivation greatly reduced the difference. However the impact of deprivation on the difference in mortality between Scotland and England and Wales has been found to have reduced since 1981; using census data from 1981, 1991 and 2001, Hanlon et al 3 found that whilst in 1981 deprivation explained over 60% of the excess mortality found in Scotland, in 1991 and 2001 deprivation explained less than half of the excess mortality. The excess mortality increased from 4.7% in 1981 to 8.2% in 2001 after adjusting for age, sex and deprivation. The largest excesses have been found in the most deprived areas of Scotland. Work published earlier this year compared the health outcomes in Glasgow with those of almost identically deprived cities Liverpool and Manchester 4, and found that premature deaths in Glasgow were over 30% higher, with the excess mortality found across men and women, all ages except the very young, and both deprived and nondeprived neighbourhoods. Approximately half of the excess premature deaths were found to be directly related to alcohol and drugs. Other recent work has investigated whether the mortality excess relates to country of residence or country of birth, as it is known that those born in Scotland who live in England and Wales have a higher mortality rate than those born in England and Wales 5, and those born in England and Wales but living in Scotland have a lower mortality rate than those born in Scotland 6. Popham et al 7 therefore compared mortality by country of birth and country of residence, with the effect of country of residence attenuated by country of birth, but not the other way round. This recent work has shown that there exist factors beyond deprivation which influence the excess mortality rate found in Glasgow. Many hypotheses have been suggested, including societal breakdown leading to self-destructive behaviours and adverse childhood experiences and the Glasgow population s response to them 4. Much of the work investigating the Glasgow Effect has focused on mortality as an outcome; it is also of interest to know whether there is a Glasgow Effect for other health outcomes and health behaviours, which themselves influence mortality. A report written for the Glasgow Centre for Population Health in examined the levels of many health behaviours in Glasgow City and Greater Glasgow compared to the rest of Scotland, and found many examples of worse health behaviours, including alcohol consumption, diet and smoking, as well as worse health outcomes, 7

11 such as higher prevalence of limiting long-term illness. A piece of work carried out by the Glasgow Centre for Population Health in compared health indicators in Greater Glasgow with those in areas across Europe. It found that Greater Glasgow had the worst levels for a number of health behaviours and health outcomes, including binge drinking, excess weekly alcohol consumption, self-assessed general health and psychological morbidity. Using data from the 1995, 1998 and 2003 Scottish Health Surveys, Gray 10 investigated the impact of living in Glasgow City, Greater Glasgow and West Central Scotland on a range of health-related factors, covering mental health, physical health and health behaviours, and the extent to which adjustment for socio-economic conditions explained any effects. The socio-economic conditions adjusted for contained both area-level and individual-level deprivation, using the Carstairs measure of area-level deprivation, social class, educational qualifications and economic activity. The study found that the levels of binge-drinking and alcohol consumption in men were higher than in the rest of Scotland, even after adjusting for Glasgow s socio-economic profile, as were the levels of psychological distress for both men and women. However adjusting for socio-economic conditions accounted for many of the worse health behaviours and outcomes in Glasgow, implying that improving Glasgow s health is strongly linked to addressing the socio-economic conditions in Glasgow. More detailed conclusions from Gray s report are discussed at the end of each section, alongside the results from the analyses carried out in this study. 1.2 Aims The overall aim of this work was to investigate whether residence in Glasgow was independently associated with poorer health outcomes and worse health behaviours compared to the rest of Scotland, after controlling for socio-economic, behavioural, biological and other health-related risk factors. The supplementary research questions are: 1. To what extent do socio-economic factors explain differences in health-related outcomes? Previous analyses have examined the role of area based deprivation in explaining poor health related outcomes in Glasgow 10, however these were based on the Carstairs measure of area-based deprivation at postcode level, which is less spatially sensitive than the Scottish Index of Multiple Deprivation (SIMD), which is measured at datazone level, with an average population of only 750. These previous analyses only controlled for socio-economic factors, and therefore did not investigate other possible explanations for the remaining effect of residence after adjusting for socio-economic factors. The analyses in this report used data from the 2008 and 2009 Scottish Health Surveys, whereas the previous analyses used data from the 1995, 1998 and 2003 Scottish Health Surveys. As part of this aim the socio-economic factor which best explained both the health outcomes and the differences between Glasgow and the rest of Scotland was investigated. 8

12 2. To what extent are differences in health-related outcomes influenced by relationship -based factors? An advantage of using the Scottish Health Survey data to investigate the Glasgow Effect is the wealth of data available, including the relationships between members of each household. There were not sufficient foster parents or adoptive parents in the study to examine their effect on the various outcomes; therefore only single parenthood and being a stepparent were examined. 3. Are aspects of social mobility significantly associated with health and healthrelated outcomes? One of the current hypotheses relating to the Glasgow Effect is the effect of social mobility 11. Therefore the effect of social mobility on both health behaviours and health-related outcomes were investigated, as well as their impact on explaining any effect of residence in Greater Glasgow and Clyde. 1.3 Methodology The combined 2008 and 2009 Scottish Health Survey datasets were used to carry out this work. The combined dataset contains information on 18,353 individuals, with 13,996 (76%) aged 16 and over, of whom 7,866 (56%) were female. A subsample from both the 2008 and 2009 surveys were selected for a nurse visit to collect biological measurements, and some of these participants agreed to provide a blood sample. As these subsamples are not representative of those who agreed to take part in the original survey, new weights were developed to allow analysis of these complete subsamples to provide results which are representative of Scotland s population. More information on the sample design and data collection is available in the 2008 and 2009 Scottish Health Survey reports 12,13. In order to investigate the Glasgow Effect an area must be identified to be compared with the rest of Scotland. Greater Glasgow and Clyde was chosen as data on Greater Glasgow and Clyde health board is more representative, and more data is available, than if just Glasgow City had been used. This made the results more robust. 3,242 adults in Greater Glasgow and Clyde provided data in the main sample, with 504 in the nurse subsample and 392 in the blood subsample. An initial logistic regression model was carried out for each outcome of interest with explanatory variables age, sex and residence in Greater Glasgow and Clyde, and a second model added SIMD quintiles. Explanatory variables were then added to this model in groups, so the new model contained the new explanatory variables as well as all the explanatory variables previously entered into the model. Backward selection was carried out after each group of variables had been added, until all the variables in the model were significant at the 5% level. More details on all variables can be found in the Scottish Health Survey 2009 main report 12. The first group of explanatory variables contained socio-economic risk factors: Income-related benefits (receiving at least one of job seekers allowance, income support or housing benefit) 9

13 National Statistics Socio-economic Classification (NS-SEC) (categorised as: managerial and professional occupations, intermediate occupations, small employers and own account workers, lower supervisory and technical occupations and semi-routine occupations, as well as a category for people for whom the NS-SEC is not applicable, such as full-time students) Economic activity (full time education, paid employment/selfemployed/government training, looking for/intending to look for work, permanently unable to work, retired, looking after home/family, doing something else) Highest educational qualifications attained (HNC/D or degree level or higher, Standard Grade or Higher Grade, other school level, none) Housing tenure (owner occupied, private rental and social rental) Marital status (single (never married or in a civil partnership), married/civil partnership and living together, married/civil partnership but separated, divorced/civil partnership legally dissolved, widowed/surviving civil partner). The next group covered behavioural risk factors: Smoking status (never/ex-occasional, ex-regular, light, moderate, heavy/don t know how many a day 14 ) Binge drinking (more than 6 units per day for women, and 8 units for men) Drinking over the recommended weekly alcohol limit (more than 14 units per week for women, and 21 units for men) Abstaining from alcohol consumption Scoring 2 or more on the CAGE questionnaire to identify potential problem drinking 15 Level of physical activity (high (30 minutes or more at least 5 days a week), medium (30 minutes or more on 1 to 4 days a week) or low (fewer than 30 minutes of activity a week)) Portions of fruit and vegetables consumed per day. The third group contained biological risk factors: Collected from everyone: BMI (<25 kg/m 2, 25 kg/m 2 and <30 kg/m 2, 30 kg/m 2 ) Collected from those who had a nurse visit: Waist-hip ratio (high if 0.95 for men, 0.85 for women) Blood pressure (normotensive untreated, normotensive treated, hypertensive untreated, hypertensive treated) Forced expiratory volume in one second Collected from those who had a blood sample taken: Total cholesterol (above or below 5 mmol/l) HDL-cholesterol (above or below 1 mmol/l) Fibrinogen (sex-specific quintiles) C-reactive protein (sex-specific quintiles). The analyses were adjusted for the complex survey design, and different survey weights were used depending on the variables included in the model. Although BMI was collected from everyone in the main sample, the other biological variables were collected during the nurse visit. As the sample size is reduced for the nurse variables, and reduced again for the blood variables, if all the blood variables 10

14 dropped out of the model then the model was re-run excluding the blood variables, and based on the nurse weights, thereby enabling a larger sample to be used. If all the nurse variables then dropped out of the model, it was re-run with only BMI added to the model, using the full sample and therefore the full sample weights. The fourth group were relationship variables and social mobility variables: Single parent Stepparent Parental NS-SEC (the higher of the mother s and father s NS-SEC) Social mobility (indicating whether the participant was upwardly mobile, downwardly mobile or stable by comparing parental NS-SEC and individual NS- SEC. Participants who did not have an NS-SEC category were not assigned a social mobility category.) Not all predictor variables were added to each model if it was not appropriate; for example BMI and high waist-hip ratio were not added to the models analysing outcomes of being overweight or obese. Any variables which were not included in the modelling are mentioned in the relevant section. All analyses were restricted to participants aged 16 plus. As not all variables have complete data, the sample size varies depending on which variables remain in the model. For direct comparisons to be made between models using odds ratios and pseudo R-squared values, for the purpose of determining the model which provides the best fit to the data (see Appendix 1), it is important to maintain a constant sample 16. Therefore after the final model was selected using all available data at each stage, the resulting models from adding each group of variables were re-run on data restricted to include participants with full data on all variables included in any of the models, and these are the results reported. Other results are reported as required. To investigate which socio-economic factor best explained the health outcome and the difference in health outcomes between Glasgow and the rest of Scotland, the final model was run using all available data, and then run containing each of SIMD, NS-SEC, economic activity, household tenure, educational qualifications, receiving income-related benefits and equivalised income in place of all the socio-economic variables (including SIMD, but excluding marital status) which were in the final model. McFadden s pseudo R 2 s were compared to find the socio-economic variable which best explained the health outcome, with the highest pseudo R 2 indicating the best model. For the models where a Glasgow Effect remained, the odds ratios for residence were compared, with the lowest odds ratio indicating the model which explained the largest proportion of the difference. 11

15 2. MENTAL AND GENERAL HEALTH SUMMARY Anxiety The factors which were found to be significantly associated with anxiety were: age, sex, residence, economic activity, potential problem drinking, abstaining from alcohol and physical activity level. Even after controlling for all of these factors, residents of Greater Glasgow and Clyde were almost twice as likely to have symptoms of moderate to high severity anxiety (92% increased risk when compared to the rest of Scotland). GHQ The factors which were found to be significantly associated with a high GHQ score (indicating possible psychiatric disorder) were: age, sex, residence, receiving income-related benefits, economic activity, educational qualifications, marital status, smoking status, potential problem drinking, abstaining from alcohol and physical activity level. Residents of Greater Glasgow and Clyde had an increased risk of having a high GHQ score even after adjusting for all of these factors (19% increased risk compared to the rest of Scotland). WEMWBS Although residence in Greater Glasgow and Clyde was associated with greater odds of having a low WEMWBS score (indicating lower levels of mental wellbeing) when age and sex were adjusted for, subsequent adjustment for SIMD accounted for all of the difference. Depression Although residence in Greater Glasgow and Clyde was associated with increased risk of depression when only age and sex were adjusted for, when age, sex and socio-economic variables, specifically NS-SEC, economic activity, equivalised income and marital status, were included in the model, the excess risk associated with residence in Greater Glasgow and Clyde was removed. Self-assessed health Despite residents of Greater Glasgow and Clyde having higher odds of poor selfassessed health when adjusting for age and sex, the so-called Glasgow Effect was fully explained when socio-economic variables were adjusted for, specifically SIMD, receiving income-related benefits, economic activity, household tenure, equivalised income, educational qualifications and NS-SEC. 12

16 2.1 Introduction The outcomes covered in this chapter are anxiety, psychological ill health, mental wellbeing, depression and self-assessed general health. Anxiety Participants were classified as suffering from anxiety if they had a score of 2 or more on the anxiety scale of the Revised Clinical Interview Schedule, indicative of symptoms of moderate to high severity. Anxiety was only measured in the nurse sample. General Health Questionnaire (GHQ-12) The General Health Questionnaire (GHQ-12) is a widely used standard measure of mental distress and psychological ill-health, consisting of 12 questions on concentration abilities, sleeping patterns, self-esteem, stress, despair, depression, and confidence in the previous few weeks. As the GHQ-12 measures deviations from people's usual functioning it cannot be used to detect chronic conditions. However it allows comparisons between groups to be investigated. Responses to the GHQ-12 items were scored, resulting in an overall score between zero and twelve. A score of four or more indicates the presence of a possible psychiatric disorder. Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS) WEMWBS is an indicator of mental wellbeing, which comprises 14 positively worded statements with a five item scale ranging from '1 - None of the time' to '5 - All of the time'. The scores therefore range from 14 to 70. A participant was classified as having a low WEMWBS score if it was more than one standard deviation below the mean. Depression Participants were classified as suffering from depression if they had a score of 2 or more on the depression scale of the Revised Clinical Interview Schedule, indicative of symptoms of moderate to high severity. Depression was only measured in the nurse sample. Self-assessed health Self-assessed health was investigated via a question asking participants to rate their health in general as 'very good', 'good', 'fair', 'bad' or 'very bad'. Those who answered bad or very bad were classified as having poor self-assessed health. It is important to note that the responses will be affected by individual perceptions, as with other self-assessed measures. 2.2 Mental Health Anxiety 9% of adults had a score of 2 or more on the anxiety scale of the Revised Clinical Interview Schedule. There were significant differences by gender, with women more likely to be display symptoms of anxiety than men (10% vs. 7%). There was a significant difference by age, with increasing prevalence of anxiety from age to a maximum at 45 54, with the pattern less clear for older ages. The prevalence 13

17 of anxiety significantly increased with increasing deprivation, with the prevalence more than twice as high in the most deprived SIMD quintile compared to the least deprived (13% vs. 6%). There was also a significant difference in prevalence of anxiety between residents of Greater Glasgow and Clyde and the rest of Scotland, with around twice the prevalence (14% vs. 7%). The model development process can be found in Appendix 2, along with McFadden s pseudo R 2 s for the different models. In view of these, the best fitting model was chosen, and the results for that model using the full data available are described here. The factors which were found to be significantly associated with anxiety were: age, sex, residence, economic activity, potential problem drinking, abstaining from alcohol and physical activity level. As anxiety was only measured in participants in the nurse sample, the sample size was 1,887. Residents of Greater Glasgow and Clyde were almost twice as likely to have moderate to severe anxiety symptoms when compared to the rest of Scotland (odds ratio of 1.92) even after adjusting for the other variables in the model. Women had significantly higher odds of having anxiety than men (odds ratio of 1.70). Age was associated with anxiety but there was no clear trend; those aged had the highest odds compared to year olds (odds ratio of 2.61). Economic activity was also associated with anxiety, with those who were looking for or intending to look for work having significantly higher odds of anxiety, compared to those in paid employment, self-employed or in government training (odds ratio of 4.06). Similarly, those who were permanently unable to work had higher odds of anxiety (odds ratio of 2.68). People who were identified as potential problem drinkers according to the CAGE questionnaire had significantly increased odds of anxiety (odds ratio of 2.88). Those who abstained from alcohol also had significantly higher odds of anxiety (odds ratio of 1.67) compared to people who drank alcohol. Whilst these findings may seem contradictory, it should be noted that a proportion of those who abstain from alcohol will do so following medical advice to stop drinking. Physical activity levels were associated with anxiety. Those whose physical activity levels were high or medium had significantly lower odds of anxiety when compared to those with low levels of physical activity, with odds ratios of 0.63 and 0.55 respectively. Table General Health Questionnaire (GHQ-12) 15% of adults had a high GHQ-12 score (indicating possible psychiatric disorder), however there were significant differences within the population. Women had a significantly higher prevalence (17% compared to 12% of men). There was also significant variation by age, although the pattern was not linear, with the highest prevalence found among year olds (17%), compared to year olds, who had the lowest prevalence of 10%. Prevalence increased with deprivation, with prevalence in the least deprived quintile (10%) half of that found in the most deprived 14

18 quintile (21%). A significant difference also existed between residents of Greater Glasgow and Clyde (18%) and the rest of Scotland (14%). The model development process can be found in Appendix 3, along with McFadden s pseudo R 2 s for the different models. In view of these, the best fitting model was chosen, and the results of that model using the full data available are presented here. It should be noted that the addition of biological variables to the model removed the effect of residence, however this model did not fit the data as well as the model described here. The results of the best fitting model were accepted over those found in other models. The factors which were found to be significantly associated with a high GHQ score were: age, sex, residence, receiving income-related benefits, economic activity, educational qualifications, marital status, smoking status, potential problem drinking, abstaining from alcohol and physical activity level. The weighted sample size with complete data was 11,088. Residents of Greater Glasgow and Clyde had an increased risk of having a high GHQ score when compared to the rest of Scotland after adjusting for all the other variables in the model (odds ratio of 1.19). Women had higher odds than men (odds ratio 1.59), and there was a significant association with age, with those aged 45 and above having lower odds than the youngest age group (16-24). Moderate and heavy smokers had an increased risk of a high GHQ score when compared to those who had never smoked or who were ex-occasional smokers (odds ratios of 1.41 and 1.47 respectively). People in receipt of income-related benefits had significantly higher odds of a high GHQ score (odds ratio of 1.35). Potential problem drinkers and abstainers both had increased risks, with the odds for potential problem drinkers more than twice as high as those who were not (odds ratio of 2.09), and abstainers were two-thirds more likely to have a high GHQ score (odds ratio of 1.67) compared to those who drank alcohol. People who were married/in a civil partnership and were living together were the least likely to have a high GHQ score. Those who were separated had the highest odds (odds ratio 1.81), followed by those who were widowed (odds ratio of 1.49). Level of educational qualifications was significantly associated with high GHQ scores but there was no clear pattern. Those who had high or medium levels of physical activity had lower odds of having a high GHQ score than those with low levels of physical activity (odds ratios of 0.67 for both categories). Economic activity was significantly associated with high GHQ scores, with those who were looking for or intending to look for work and those who were permanently unable to work having the greatest risk of a high GHQ score when compared to those in paid employment, self-employed or government training (odds ratios of 3.36 and 2.91 respectively). Table 2 15

19 2.2.3 WEMWBS A similar pattern was found for having a low WEMWBS score as for having a high GHQ score. Overall 15% of adults in Scotland had a low WEMWBS score (defined as one standard deviation or more below the mean score), however there were significant differences within the population. Women had a significantly higher prevalence of low WEMWBS scores (16% compared to 14% of men). There was significant variation by age, although the pattern was not linear, with the highest prevalence found among those aged 75 and over (18%), whereas year olds had the lowest prevalence (12%), followed by year olds (13%). Prevalence increased with deprivation, with the prevalence in the least deprived quintile (8%) just over a third of that found in the most deprived quintile (23%). A significant difference in prevalence also existed between residents of Greater Glasgow and Clyde (17%) and the rest of Scotland (14%). In the initial logistic regression model containing only age, sex and residence, residence in Greater Glasgow and Clyde had an odds ratio of 1.25, meaning that participants who resided in Greater Glasgow and Clyde had 25% increased odds of having a low WEMWBS score, compared to the rest of the Scotland. However once the model was also adjusted for SIMD the odds ratio dropped to 1.10, which was not significant at the 5% level. SIMD was highly significant, with increasing odds of poor mental wellbeing with increasing deprivation. This indicates that the different distribution of SIMD in Greater Glasgow and Clyde compared to the rest of Scotland explains the difference in prevalence of low WEMWBS scores Depression 8% of adults had two or more symptoms of depression on the Revised Clinical Interview Schedule, with significantly higher rates in women (10%) than men (7%). There was also significant difference by age, although there was no clear linear pattern. Prevalence of depression increased from age (4%) to (11%), but then decreased before increasing again for those age 75 and over. Residence was also significantly associated with depression, with a much higher prevalence among residents of Greater Glasgow and Clyde (13%) than the rest of Scotland (7%). The prevalence of depression increased with increasing deprivation, from 5% among those in the least deprived SIMD quintile to 13% in the most deprived SIMD quintile. In the initial logistic regression model containing only age, sex and residence, residence in Greater Glasgow and Clyde had an odds ratio of 1.80 indicating that residents of Greater Glasgow and Clyde s odds of having moderate to severe depression were 80% higher than the rest of Scotland, after adjusting for age and sex. When SIMD was added to the model the odds ratio decreased to 1.66, showing that a small amount of the Glasgow Effect has been accounted for by deprivation. When the group of socio-economic variables were added to the model and backward selection performed, residence was no longer significant at the 5% level. The variables which remained in the model (with p<0.05) were age, sex, NS-SEC, economic activity, equivalised income and marital status. When residence dropped from the model only these variables remained in the model, showing that these variables fully explained the effect previously observed from residing in Greater Glasgow and Clyde. 16

20 2.3 General Health Self-assessed health 7% of adults rated their health as bad or very bad, with no significant difference between men and women. The prevalence increased with increasing age, from 1% of year olds to 14% of those aged 75 and over. There was a large significant difference in prevalence by SIMD, with prevalence decreasing from 14% in the most deprived quintile to 3% in the least deprived quintile. Prevalence of poor selfassessed health was significantly higher for those living in Greater Glasgow and Clyde than for the rest of Scotland (9% vs. 4%). In the initial logistic regression model containing age, sex and residence, residents of Greater Glasgow and Clyde had an odds ratio of 1.56 of having poor self-assessed health, meaning they were 56% more likely than those in the rest of Scotland to report poor health. When SIMD was added to the model all four variables were significant predictors of having poor self-assessed health, with the odds ratio for Greater Glasgow and Clyde reduced to 1.20, indicating that more than half of the Glasgow Effect in relation to poor self-assessed health was explained by SIMD. However there was still a 20% increase in odds of having poor self-assessed health for residents of Greater Glasgow and Clyde compared to the rest of Scotland. When socio-economic variables were added to the model and backward selection performed, residence was no longer significant at the 5% level. The variables which remained in the model (with p<0.05) were age, sex, SIMD, receiving income-related benefits, economic activity, household tenure, equivalised income, and educational qualifications. When residence dropped from the model NS-SEC was also in the model. This finding suggests that these socio-economic variables, alongside SIMD, explain the apparent difference in rates of poor self-assessed health between Greater Glasgow and Clyde and the rest of Scotland. 2.4 Assessing the impact of the socio-economic variables individually When examined individually, the socio-economic variable which provided the best-fit model for the mental and general health outcomes was economic activity as it had the highest McFadden s pseudo R 2, indicating that this measure was the best predictor of the general and mental health outcomes after adjusting for the rest of the variables in the final models. For anxiety and GHQ it was also possible to see which socio-economic variable best explained the difference between Greater Glasgow and Clyde and the rest of Scotland; equivalised income explained the most difference for anxiety, and economic activity for GHQ. 2.5 Conclusions and Discussion Despite adjusting for age and sex, residents of Greater Glasgow and Clyde had increased odds of having bad or very bad self-assessed health compared to the rest of Scotland. Further adjusting for SIMD partly attenuated this increased risk and adjusting for a wider range of socio-economic variables fully attenuated the increased risk, removing the so called Glasgow Effect. Previous analyses 10 investigating Greater Glasgow using the 1995, 1998 and 2003 Scottish Health 17

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